ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline

Swier Garst; Lieke Kuiper; Erik van den Akker; Niels van den Berg; Mohsen Ghanbari; Simon Mooijaart; Marian Beekman; Marcel Reinders; P. Eline Slagboom; Joyce van Meurs

doi:10.1016/j.crmeth.2026.101405

ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline

Swier Garst
, Lieke Kuiper
, Erik van den Akker
, Niels van den Berg
, Mohsen Ghanbari
, Simon Mooijaart
, Marian Beekman
, Marcel Reinders^*
, P. Eline Slagboom^*
, Joyce van Meurs^*

^*Corresponding author for this work

Delft University of Technology
Leiden University
National Institute of Public Health and the Environment
Leids Universitair Medisch Centrum

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

Many molecular aging biomarkers have been developed to capture heterogeneity in individual aging rates. Yet, systematic comparison of the modeling choices underlying these biomarkers has been limited. In this study, we trained aging biomarkers on the Rockwood frailty index (FI) and all-cause mortality using UK Biobank Olink proteomics and metabolomics (¹H-NMR) data (n = 40,696). We systematically established the impact of model choice, target outcome, and molecular data source on several age-related outcomes. From this, we developed two aging biomarkers, ProteinFrailty (ProtFI) and ProteinMortality (ProtMort), which are both ElasticNet models that use a minimal set of proteins to predict FI and mortality, respectively. In particular, ProtFI outperformed established aging biomarkers in relation to diverse outcomes, including incident cardiovascular disease, handgrip strength, and self-rated health, both in internal validation and two Dutch external cohorts (n = 995, n = 500). Our findings show that an efficient frailty-trained proteomic biomarker robustly predicts age-related decline.

Original language	English
Article number	101405
Journal	Cell Reports Methods
Volume	6
Issue number	5
DOIs	https://doi.org/10.1016/j.crmeth.2026.101405
Publication status	Published - 18 May 2026

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© 2026 The Authors

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ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related declineFinal published version, 4.48 MBLicence: CC BY

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title = "ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline",

abstract = "Many molecular aging biomarkers have been developed to capture heterogeneity in individual aging rates. Yet, systematic comparison of the modeling choices underlying these biomarkers has been limited. In this study, we trained aging biomarkers on the Rockwood frailty index (FI) and all-cause mortality using UK Biobank Olink proteomics and metabolomics (1H-NMR) data (n = 40,696). We systematically established the impact of model choice, target outcome, and molecular data source on several age-related outcomes. From this, we developed two aging biomarkers, ProteinFrailty (ProtFI) and ProteinMortality (ProtMort), which are both ElasticNet models that use a minimal set of proteins to predict FI and mortality, respectively. In particular, ProtFI outperformed established aging biomarkers in relation to diverse outcomes, including incident cardiovascular disease, handgrip strength, and self-rated health, both in internal validation and two Dutch external cohorts (n = 995, n = 500). Our findings show that an efficient frailty-trained proteomic biomarker robustly predicts age-related decline.",

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doi = "10.1016/j.crmeth.2026.101405",

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T1 - ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline

AU - Garst, Swier

AU - Kuiper, Lieke

AU - van den Akker, Erik

AU - van den Berg, Niels

AU - Ghanbari, Mohsen

AU - Mooijaart, Simon

AU - Beekman, Marian

AU - Reinders, Marcel

AU - Slagboom, P. Eline

AU - van Meurs, Joyce

PY - 2026/5/18

Y1 - 2026/5/18

N2 - Many molecular aging biomarkers have been developed to capture heterogeneity in individual aging rates. Yet, systematic comparison of the modeling choices underlying these biomarkers has been limited. In this study, we trained aging biomarkers on the Rockwood frailty index (FI) and all-cause mortality using UK Biobank Olink proteomics and metabolomics (1H-NMR) data (n = 40,696). We systematically established the impact of model choice, target outcome, and molecular data source on several age-related outcomes. From this, we developed two aging biomarkers, ProteinFrailty (ProtFI) and ProteinMortality (ProtMort), which are both ElasticNet models that use a minimal set of proteins to predict FI and mortality, respectively. In particular, ProtFI outperformed established aging biomarkers in relation to diverse outcomes, including incident cardiovascular disease, handgrip strength, and self-rated health, both in internal validation and two Dutch external cohorts (n = 995, n = 500). Our findings show that an efficient frailty-trained proteomic biomarker robustly predicts age-related decline.

AB - Many molecular aging biomarkers have been developed to capture heterogeneity in individual aging rates. Yet, systematic comparison of the modeling choices underlying these biomarkers has been limited. In this study, we trained aging biomarkers on the Rockwood frailty index (FI) and all-cause mortality using UK Biobank Olink proteomics and metabolomics (1H-NMR) data (n = 40,696). We systematically established the impact of model choice, target outcome, and molecular data source on several age-related outcomes. From this, we developed two aging biomarkers, ProteinFrailty (ProtFI) and ProteinMortality (ProtMort), which are both ElasticNet models that use a minimal set of proteins to predict FI and mortality, respectively. In particular, ProtFI outperformed established aging biomarkers in relation to diverse outcomes, including incident cardiovascular disease, handgrip strength, and self-rated health, both in internal validation and two Dutch external cohorts (n = 995, n = 500). Our findings show that an efficient frailty-trained proteomic biomarker robustly predicts age-related decline.

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DO - 10.1016/j.crmeth.2026.101405

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SN - 2667-2375

VL - 6

JO - Cell Reports Methods

JF - Cell Reports Methods

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ER -

ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline

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